Abstract
Developing robust and universal methods for unsupervised segmentation of moving objects in video sequences has proved to be a hard and challenging task. The best solutions are, in general, computationally heavy preventing their use in real-time applications. This research addresses this problem by proposing a robust and computationally efficient method, BMOG, that significantly boosts the performance of the widely used MOG2 method. The complexity of BMOG is kept low, proving its suitability for real-time applications. The proposed solution explores a novel classification mechanism that combines color space discrimination capabilities with hysteresis and a dynamic learning rate for background model update.
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Acknowledgments
This work has received financial support from the Xunta de Galicia (Agrupación Estratéxica Consolidada de Galicia accreditation 2016-2019) and the European Union (European Regional Development Fund - ERDF) and research contract GRC2014/024 (Modalidade: Grupos de Referencia Competitiva 2014) and project “TEC4Growth - Pervasive Intelligence, Enhancers and Proofs of Concept with Industrial Impact/NORTE-01-0145-FEDER-000020”, financed by the North Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, and through the European Regional Development Fund (ERDF).
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Martins, I., Carvalho, P., Corte-Real, L., Alba-Castro, J.L. (2017). BMOG: Boosted Gaussian Mixture Model with Controlled Complexity. In: Alexandre, L., Salvador Sánchez, J., Rodrigues, J. (eds) Pattern Recognition and Image Analysis. IbPRIA 2017. Lecture Notes in Computer Science(), vol 10255. Springer, Cham. https://doi.org/10.1007/978-3-319-58838-4_6
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DOI: https://doi.org/10.1007/978-3-319-58838-4_6
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